ts-scikit-optimize 0.9.2

Important links

• Static documentation - Static documentation

• Example notebooks - can be found in examples.

• Issue tracker - https://github.com/scikit-optimize/scikit-optimize/issues

• Releases - https://pypi.python.org/pypi/scikit-optimize

Install

scikit-optimize requires

• Python >= 3.6

• NumPy (>= 1.13.3)

• SciPy (>= 0.19.1)

• joblib (>= 0.11)

• scikit-learn >= 0.20

• matplotlib >= 2.0.0

You can install the latest release with:

pip install ts-scikit-optimize

This installs an essential version of scikit-optimize. To install scikit-optimize with plotting functionality, you can instead do:

pip install 'ts-scikit-optimize[plots]'

This will install matplotlib along with scikit-optimize.

In addition there is a conda-forge package of scikit-optimize:

conda install -c conda-forge scikit-optimize

Using conda-forge is probably the easiest way to install scikit-optimize on Windows.

Getting started

Find the minimum of the noisy function f(x) over the range -2 < x < 2 with skopt:

import numpy as np
from skopt import gp_minimize

def f(x):
    return (np.sin(5 * x[0]) * (1 - np.tanh(x[0] ** 2)) +
            np.random.randn() * 0.1)

res = gp_minimize(f, [(-2.0, 2.0)])

For more control over the optimization loop you can use the skopt.Optimizer class:

from skopt import Optimizer

opt = Optimizer([(-2.0, 2.0)])

for i in range(20):
    suggested = opt.ask()
    y = f(suggested)
    opt.tell(suggested, y)
    print('iteration:', i, suggested, y)

Read our introduction to bayesian optimization and the other examples.

Development

The library is still experimental and under heavy development. Checkout the next milestone for the plans for the next release or look at some easy issues to get started contributing.

The development version can be installed through:

git clone https://github.com/khulnasoft-lab/ts-scikit-optimize.git
cd ts-scikit-optimize
pip install -e.

Run all tests by executing pytest in the top level directory.

To only run the subset of tests with short run time, you can use pytest -m 'fast_test' (pytest -m 'slow_test' is also possible). To exclude all slow running tests try pytest -m 'not slow_test'.

This is implemented using pytest attributes. If a tests runs longer than 1 second, it is marked as slow, else as fast.

All contributors are welcome!

Commercial support

Feel free to get in touch if you need commercial support or would like to sponsor development. Resources go towards paying for additional work by seasoned engineers and researchers.

Made possible by

The scikit-optimize project was made possible with the support of

If your employer allows you to work on scikit-optimize during the day and would like recognition, feel free to add them to the “Made possible by” list.